NeuroFit: a robust and scalable synthetic data-driven deep learning solution for automated borehole image analysis at LWD and wireline resolution

Borehole images (BHI) are crucial for resource exploration, providing detailed fracture analysis at millimeter-scale resolution. However, their interpretation is typically carried out manually, a process that is time-consuming, costly, and subject to significant uncertainty due to interpreter bias and variability. Current state-of-the-art AI methods for automated or semi-automated fracture analysis of BHI often rely on field data for training, using manual interpretations as labels. This approach inherently embeds both aleatoric (data-related) and epistemic (manual) uncertainties, which may undermine the reliability and adaptability of these methods. This study proposes an alternative, synthetic data-driven approach to train a set of two deep neural networks (DNNs) connected in sequence. These DNNs are designed to replicate the primary cognitive tasks involved in manual interpretation: the segmentation of the BHI to identify potential edge zones and the tracing of sinusoids over these edges to approximate their best-fitting 2D representation. By utilizing synthetic data, we are able to systematically assess the sensitivity of both networks and explore various training strategies, including curriculum learning (CL) and self-attention mechanisms. Our proposed solution is designed for post-hoc human-machine collaboration, where the model supports but does not replace human expertise. This framework enables the possibility of a multi-level uncertainty assessment—at the human, machine, and human-machine interface levels—opening the door to new ways of understanding and quantifying the sources of uncertainty in BHI analysis. Additionally, the synthetic data-driven approach ensures the generalizability and scalability of the method, as demonstrated by its successful application to low-resolution logging-while-drilling (LWD) and high-resolution fullbore formation microimager (FMI) datasets from multiple global locations. By combining advanced AI techniques with geoscientific knowledge, this study outlines a potential pathway toward more robust, ethical, and sustainable fracture analysis workflows. Beyond the traditional benefits of reduced cost and time, the approach may provide a scientifically grounded framework for exploring the benefits of human-machine collaboration and uncertainty quantification in geoscience practices. If adopted, this framework could significantly advance the field of BHI analysis, offering new tools for resource exploration in hydrocarbon and geothermal applications.